Overview

Dataset statistics

Number of variables79
Number of observations10100
Missing cells519148
Missing cells (%)65.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory611.0 B

Variable types

Categorical9
DateTime1
Boolean47
Numeric22

Warnings

chills has constant value "True" Constant
cough has constant value "True" Constant
diarrhoea has constant value "True" Constant
event_admission has constant value "True" Constant
event_enrolment has constant value "True" Constant
feeling_faint has constant value "True" Constant
sore_throat has constant value "True" Constant
spleen_palpation has constant value "False" Constant
study_no has a high cardinality: 664 distinct values High cardinality
haematocrit is highly correlated with haematocrit_percent and 7 other fieldsHigh correlation
haematocrit_percent is highly correlated with haematocrit and 2 other fieldsHigh correlation
haemoglobin is highly correlated with haematocrit and 2 other fieldsHigh correlation
joint_pain_level is highly correlated with muscle_pain_levelHigh correlation
liver_size is highly correlated with haematocrit_percent and 3 other fieldsHigh correlation
lymphocytes_percent is highly correlated with haematocritHigh correlation
monocytes_percent is highly correlated with haematocritHigh correlation
muscle_pain_level is highly correlated with joint_pain_levelHigh correlation
neutrophils_percent is highly correlated with haematocritHigh correlation
plt is highly correlated with haematocritHigh correlation
wbc is highly correlated with haematocrit and 1 other fieldsHigh correlation
weight is highly correlated with haematocrit and 1 other fieldsHigh correlation
abdominal_tenderness has 7725 (76.5%) missing values Missing
albumin has 7831 (77.5%) missing values Missing
alt has 7838 (77.6%) missing values Missing
ascites has 7725 (76.5%) missing values Missing
ast has 7840 (77.6%) missing values Missing
bleeding has 7725 (76.5%) missing values Missing
bleeding_gum has 7723 (76.5%) missing values Missing
bleeding_nose has 7723 (76.5%) missing values Missing
bleeding_other has 10082 (99.8%) missing values Missing
bleeding_vaginal has 7723 (76.5%) missing values Missing
body_temperature has 7453 (73.8%) missing values Missing
bruising has 7723 (76.5%) missing values Missing
chills has 9490 (94.0%) missing values Missing
clinical_shock has 7725 (76.5%) missing values Missing
conjunctival has 7725 (76.5%) missing values Missing
convulsions has 7725 (76.5%) missing values Missing
cough has 9738 (96.4%) missing values Missing
creatine_kinase has 7842 (77.6%) missing values Missing
diarrhoea has 9818 (97.2%) missing values Missing
event_admission has 9436 (93.4%) missing values Missing
event_enrolment has 9436 (93.4%) missing values Missing
feeling_faint has 9636 (95.4%) missing values Missing
fetal has 9536 (94.4%) missing values Missing
fibrinogen has 7873 (78.0%) missing values Missing
haematocrit has 10057 (99.6%) missing values Missing
haematocrit_percent has 5743 (56.9%) missing values Missing
haemoglobin has 5743 (56.9%) missing values Missing
hematemesis has 7723 (76.5%) missing values Missing
hematuria has 7723 (76.5%) missing values Missing
inr has 7835 (77.6%) missing values Missing
jaundice has 7725 (76.5%) missing values Missing
lethargy has 7725 (76.5%) missing values Missing
liver_palpation has 7725 (76.5%) missing values Missing
lymphadenopathy has 7725 (76.5%) missing values Missing
lymphocytes_percent has 5744 (56.9%) missing values Missing
melaena has 7723 (76.5%) missing values Missing
meningism has 7725 (76.5%) missing values Missing
monocytes_percent has 5744 (56.9%) missing values Missing
nausea has 9603 (95.1%) missing values Missing
neurology has 7725 (76.5%) missing values Missing
neutrophils_percent has 5743 (56.9%) missing values Missing
oedema has 7370 (73.0%) missing values Missing
oedema_face has 7723 (76.5%) missing values Missing
oedema_feet has 7723 (76.5%) missing values Missing
oedema_hands has 7723 (76.5%) missing values Missing
petechiae has 7723 (76.5%) missing values Missing
pharyngeal has 7725 (76.5%) missing values Missing
pleural_effusion has 7725 (76.5%) missing values Missing
plt has 5744 (56.9%) missing values Missing
pulse has 7453 (73.8%) missing values Missing
rales_crackles has 7725 (76.5%) missing values Missing
respiratory_distress has 7725 (76.5%) missing values Missing
respiratory_rate has 7453 (73.8%) missing values Missing
restlessness has 7725 (76.5%) missing values Missing
rhinitis has 7725 (76.5%) missing values Missing
sbp has 7453 (73.8%) missing values Missing
skin_flush has 7725 (76.5%) missing values Missing
skin_rash has 7725 (76.5%) missing values Missing
sore_throat has 9912 (98.1%) missing values Missing
spleen_palpation has 7725 (76.5%) missing values Missing
tck has 7874 (78.0%) missing values Missing
tq has 7870 (77.9%) missing values Missing
uterus_tender has 9173 (90.8%) missing values Missing
vagina_loss has 9168 (90.8%) missing values Missing
wbc has 5743 (56.9%) missing values Missing
weight has 6669 (66.0%) missing values Missing
study_no is uniformly distributed Uniform
bleeding_other is uniformly distributed Uniform

Reproduction

Analysis started2021-01-24 10:50:15.601118
Analysis finished2021-01-24 10:51:09.697329
Duration54.1 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Categorical

HIGH CARDINALITY
UNIFORM

Distinct664
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
03-5431
 
27
03-5425
 
27
03-5010
 
26
03-5007
 
25
03-5443
 
25
Other values (659)
9970 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03-1001
2nd row03-1001
3rd row03-1001
4th row03-1001
5th row03-1001
ValueCountFrequency (%)
03-543127
 
0.3%
03-542527
 
0.3%
03-501026
 
0.3%
03-500725
 
0.2%
03-544325
 
0.2%
03-538124
 
0.2%
03-536024
 
0.2%
03-540723
 
0.2%
03-526023
 
0.2%
03-520223
 
0.2%
Other values (654)9853
97.6%
2021-01-24T11:51:09.884589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03-543127
 
0.3%
03-542527
 
0.3%
03-501026
 
0.3%
03-500725
 
0.2%
03-544325
 
0.2%
03-538124
 
0.2%
03-536024
 
0.2%
03-540723
 
0.2%
03-526023
 
0.2%
03-520223
 
0.2%
Other values (654)9853
97.6%

Most occurring characters

ValueCountFrequency (%)
014510
20.5%
313680
19.3%
512781
18.1%
-10100
14.3%
14465
 
6.3%
43730
 
5.3%
23573
 
5.1%
62075
 
2.9%
91945
 
2.8%
81930
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number60600
85.7%
Dash Punctuation10100
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
014510
23.9%
313680
22.6%
512781
21.1%
14465
 
7.4%
43730
 
6.2%
23573
 
5.9%
62075
 
3.4%
91945
 
3.2%
81930
 
3.2%
71911
 
3.2%
ValueCountFrequency (%)
-10100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common70700
100.0%

Most frequent character per script

ValueCountFrequency (%)
014510
20.5%
313680
19.3%
512781
18.1%
-10100
14.3%
14465
 
6.3%
43730
 
5.3%
23573
 
5.1%
62075
 
2.9%
91945
 
2.8%
81930
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII70700
100.0%

Most frequent character per block

ValueCountFrequency (%)
014510
20.5%
313680
19.3%
512781
18.1%
-10100
14.3%
14465
 
6.3%
43730
 
5.3%
23573
 
5.1%
62075
 
2.9%
91945
 
2.8%
81930
 
2.7%

date
Date

Distinct4523
Distinct (%)44.9%
Missing27
Missing (%)0.3%
Memory size79.0 KiB
Minimum2016-10-05 00:00:00
Maximum2018-05-17 00:00:00
2021-01-24T11:51:09.985917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:10.107286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
nan
9774 
1.0
 
279
2.0
 
45
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30300
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan9774
96.8%
1.0279
 
2.8%
2.045
 
0.4%
3.02
 
< 0.1%
2021-01-24T11:51:10.306378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:51:10.364379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan9774
96.8%
1.0279
 
2.8%
2.045
 
0.4%
3.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n19548
64.5%
a9774
32.3%
.326
 
1.1%
0326
 
1.1%
1279
 
0.9%
245
 
0.1%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29322
96.8%
Decimal Number652
 
2.2%
Other Punctuation326
 
1.1%

Most frequent character per category

ValueCountFrequency (%)
0326
50.0%
1279
42.8%
245
 
6.9%
32
 
0.3%
ValueCountFrequency (%)
n19548
66.7%
a9774
33.3%
ValueCountFrequency (%)
.326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29322
96.8%
Common978
 
3.2%

Most frequent character per script

ValueCountFrequency (%)
.326
33.3%
0326
33.3%
1279
28.5%
245
 
4.6%
32
 
0.2%
ValueCountFrequency (%)
n19548
66.7%
a9774
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30300
100.0%

Most frequent character per block

ValueCountFrequency (%)
n19548
64.5%
a9774
32.3%
.326
 
1.1%
0326
 
1.1%
1279
 
0.9%
245
 
0.1%
32
 
< 0.1%

abdominal_tenderness
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2092 
True
 
283
(Missing)
7725 
ValueCountFrequency (%)
False2092
 
20.7%
True283
 
2.8%
(Missing)7725
76.5%
2021-01-24T11:51:10.412883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

age
Real number (ℝ≥0)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.87108911
Minimum18
Maximum45
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:10.479994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q123
median27
Q330
95-th percentile35
Maximum45
Range27
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.720488742
Coefficient of variation (CV)0.1756716567
Kurtosis-0.264299352
Mean26.87108911
Median Absolute Deviation (MAD)3
Skewness0.2444607218
Sum271398
Variance22.28301396
MonotocityNot monotonic
2021-01-24T11:51:10.584997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
27918
 
9.1%
25893
 
8.8%
26789
 
7.8%
30737
 
7.3%
28724
 
7.2%
23703
 
7.0%
24678
 
6.7%
29565
 
5.6%
33565
 
5.6%
32430
 
4.3%
Other values (15)3098
30.7%
ValueCountFrequency (%)
18247
2.4%
19394
3.9%
20377
3.7%
21421
4.2%
22393
3.9%
ValueCountFrequency (%)
4517
0.2%
4112
0.1%
4019
0.2%
3929
0.3%
3827
0.3%

albumin
Real number (ℝ≥0)

MISSING

Distinct225
Distinct (%)9.9%
Missing7831
Missing (%)77.5%
Infinite0
Infinite (%)0.0%
Mean37.87831644
Minimum23.5
Maximum51.3
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:10.700662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile30.1
Q134.6
median38.1
Q341.3
95-th percentile45.1
Maximum51.3
Range27.8
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation4.598959566
Coefficient of variation (CV)0.1214140437
Kurtosis-0.4630757061
Mean37.87831644
Median Absolute Deviation (MAD)3.3
Skewness-0.1403309986
Sum85945.9
Variance21.15042909
MonotocityNot monotonic
2021-01-24T11:51:10.818707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.428
 
0.3%
4024
 
0.2%
40.424
 
0.2%
39.324
 
0.2%
38.823
 
0.2%
35.223
 
0.2%
39.423
 
0.2%
34.222
 
0.2%
41.622
 
0.2%
38.422
 
0.2%
Other values (215)2034
 
20.1%
(Missing)7831
77.5%
ValueCountFrequency (%)
23.51
< 0.1%
23.81
< 0.1%
24.41
< 0.1%
24.71
< 0.1%
25.71
< 0.1%
ValueCountFrequency (%)
51.31
< 0.1%
50.21
< 0.1%
49.61
< 0.1%
49.51
< 0.1%
49.11
< 0.1%

alt
Real number (ℝ≥0)

MISSING

Distinct420
Distinct (%)18.6%
Missing7838
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean81.80910698
Minimum5
Maximum988
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:10.941231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q125
median47
Q3100.825
95-th percentile255
Maximum988
Range983
Interquartile range (IQR)75.825

Descriptive statistics

Standard deviation97.41557869
Coefficient of variation (CV)1.190766924
Kurtosis16.88983276
Mean81.80910698
Median Absolute Deviation (MAD)29
Skewness3.3831445
Sum185052.2
Variance9489.794972
MonotocityNot monotonic
2021-01-24T11:51:11.088704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2146
 
0.5%
1842
 
0.4%
1939
 
0.4%
2739
 
0.4%
1535
 
0.3%
1735
 
0.3%
2635
 
0.3%
1434
 
0.3%
1633
 
0.3%
3333
 
0.3%
Other values (410)1891
 
18.7%
(Missing)7838
77.6%
ValueCountFrequency (%)
54
 
< 0.1%
75
 
< 0.1%
7.11
 
< 0.1%
811
0.1%
913
0.1%
ValueCountFrequency (%)
9881
< 0.1%
9711
< 0.1%
8112
< 0.1%
7101
< 0.1%
6892
< 0.1%

anorexia
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
True
9333 
False
 
767
ValueCountFrequency (%)
True9333
92.4%
False767
 
7.6%
2021-01-24T11:51:11.164317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ascites
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2374 
True
 
1
(Missing)
7725 
ValueCountFrequency (%)
False2374
 
23.5%
True1
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:11.202219image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ast
Real number (ℝ≥0)

MISSING

Distinct471
Distinct (%)20.8%
Missing7840
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean102.5814602
Minimum11
Maximum1000
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:11.283715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile17.58
Q130
median58
Q3120.325
95-th percentile339.05
Maximum1000
Range989
Interquartile range (IQR)90.325

Descriptive statistics

Standard deviation128.111302
Coefficient of variation (CV)1.248873839
Kurtosis14.57590997
Mean102.5814602
Median Absolute Deviation (MAD)33
Skewness3.354431735
Sum231834.1
Variance16412.50569
MonotocityNot monotonic
2021-01-24T11:51:11.413115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2945
 
0.4%
2441
 
0.4%
1939
 
0.4%
2338
 
0.4%
2638
 
0.4%
2238
 
0.4%
3138
 
0.4%
2137
 
0.4%
2835
 
0.3%
4331
 
0.3%
Other values (461)1880
 
18.6%
(Missing)7840
77.6%
ValueCountFrequency (%)
112
 
< 0.1%
129
0.1%
12.91
 
< 0.1%
137
0.1%
13.51
 
< 0.1%
ValueCountFrequency (%)
10001
< 0.1%
9881
< 0.1%
9821
< 0.1%
9461
< 0.1%
9431
< 0.1%

bleeding
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
True
1452 
False
923 
(Missing)
7725 
ValueCountFrequency (%)
True1452
 
14.4%
False923
 
9.1%
(Missing)7725
76.5%
2021-01-24T11:51:11.487045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_gum
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2279 
True
 
98
(Missing)
7723 
ValueCountFrequency (%)
False2279
 
22.6%
True98
 
1.0%
(Missing)7723
76.5%
2021-01-24T11:51:11.524011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
False
9931 
True
 
169
ValueCountFrequency (%)
False9931
98.3%
True169
 
1.7%
2021-01-24T11:51:11.558381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_nose
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2336 
True
 
41
(Missing)
7723 
ValueCountFrequency (%)
False2336
 
23.1%
True41
 
0.4%
(Missing)7723
76.5%
2021-01-24T11:51:11.593683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_other
Categorical

MISSING
UNIFORM

Distinct16
Distinct (%)88.9%
Missing10082
Missing (%)99.8%
Memory size79.0 KiB
FEW VAGINAL DISCHARGE
VAGINAL LEAKAGE DUE TO MISCARRIAGE
 
1
HEMOTYPSIE
 
1
RECOVERY RASH ON 2 LEGS , SMALL AMOUNT OF BROWN FLUID FROM VAGINA
 
1
BLEEDING FROM HEMORRHOIDS
 
1
Other values (11)
11 

Length

Max length65
Median length28
Mean length27
Min length6

Characters and Unicode

Total characters486
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)83.3%

Sample

1st rowVAGINAL LEAKAGE DUE TO MISCARRIAGE
2nd rowHEMOTYPSY
3rd rowSMALL AMOUNT OF BROWN FLUID FROM VAGINA
4th rowRECOVERY RASH ON 2 LEGS , SMALL AMOUNT OF BROWN FLUID FROM VAGINA
5th rowCONJUNCTIVA BLEEDING IN THE RIGHT EYE.
ValueCountFrequency (%)
FEW VAGINAL DISCHARGE3
 
< 0.1%
VAGINAL LEAKAGE DUE TO MISCARRIAGE1
 
< 0.1%
HEMOTYPSIE1
 
< 0.1%
RECOVERY RASH ON 2 LEGS , SMALL AMOUNT OF BROWN FLUID FROM VAGINA1
 
< 0.1%
BLEEDING FROM HEMORRHOIDS1
 
< 0.1%
HEMOTYPSY1
 
< 0.1%
CONJUNCTIVA BLEEDING IN THE RIGHT EYE.1
 
< 0.1%
LOCHIA1
 
< 0.1%
MILD HEMOPTYSIS1
 
< 0.1%
LOCHIA REDUCED1
 
< 0.1%
Other values (6)6
 
0.1%
(Missing)10082
99.8%
2021-01-24T11:51:11.798105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bleeding7
 
9.2%
in6
 
7.9%
eye5
 
6.6%
conjunctival4
 
5.3%
vaginal4
 
5.3%
right4
 
5.3%
23
 
3.9%
discharge3
 
3.9%
from3
 
3.9%
few3
 
3.9%
Other values (25)34
44.7%

Most occurring characters

ValueCountFrequency (%)
58
 
11.9%
E48
 
9.9%
I42
 
8.6%
N36
 
7.4%
A32
 
6.6%
L26
 
5.3%
O25
 
5.1%
G23
 
4.7%
C20
 
4.1%
R20
 
4.1%
Other values (17)156
32.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter421
86.6%
Space Separator58
 
11.9%
Other Punctuation4
 
0.8%
Decimal Number3
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
E48
 
11.4%
I42
 
10.0%
N36
 
8.6%
A32
 
7.6%
L26
 
6.2%
O25
 
5.9%
G23
 
5.5%
C20
 
4.8%
R20
 
4.8%
T18
 
4.3%
Other values (13)131
31.1%
ValueCountFrequency (%)
.3
75.0%
,1
 
25.0%
ValueCountFrequency (%)
58
100.0%
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin421
86.6%
Common65
 
13.4%

Most frequent character per script

ValueCountFrequency (%)
E48
 
11.4%
I42
 
10.0%
N36
 
8.6%
A32
 
7.6%
L26
 
6.2%
O25
 
5.9%
G23
 
5.5%
C20
 
4.8%
R20
 
4.8%
T18
 
4.3%
Other values (13)131
31.1%
ValueCountFrequency (%)
58
89.2%
23
 
4.6%
.3
 
4.6%
,1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII486
100.0%

Most frequent character per block

ValueCountFrequency (%)
58
 
11.9%
E48
 
9.9%
I42
 
8.6%
N36
 
7.4%
A32
 
6.6%
L26
 
5.3%
O25
 
5.1%
G23
 
4.7%
C20
 
4.1%
R20
 
4.1%
Other values (17)156
32.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
False
9964 
True
 
136
ValueCountFrequency (%)
False9964
98.7%
True136
 
1.3%
2021-01-24T11:51:11.864789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vaginal
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2113 
True
 
264
(Missing)
7723 
ValueCountFrequency (%)
False2113
 
20.9%
True264
 
2.6%
(Missing)7723
76.5%
2021-01-24T11:51:11.906122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

body_temperature
Real number (ℝ≥0)

MISSING

Distinct39
Distinct (%)1.5%
Missing7453
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean37.69852663
Minimum36.7
Maximum41
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:11.985014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum36.7
5-th percentile37
Q137.1
median37.3
Q338
95-th percentile39.5
Maximum41
Range4.3
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.8759498147
Coefficient of variation (CV)0.02323565117
Kurtosis0.5322576867
Mean37.69852663
Median Absolute Deviation (MAD)0.3
Skewness1.299861569
Sum99788
Variance0.7672880778
MonotocityNot monotonic
2021-01-24T11:51:12.101158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
37633
 
6.3%
37.2426
 
4.2%
37.3234
 
2.3%
37.1230
 
2.3%
39200
 
2.0%
38156
 
1.5%
37.4147
 
1.5%
37.5100
 
1.0%
39.581
 
0.8%
38.573
 
0.7%
Other values (29)367
 
3.6%
(Missing)7453
73.8%
ValueCountFrequency (%)
36.71
 
< 0.1%
36.82
 
< 0.1%
36.93
 
< 0.1%
37633
6.3%
37.1230
 
2.3%
ValueCountFrequency (%)
414
< 0.1%
40.61
 
< 0.1%
40.52
< 0.1%
40.42
< 0.1%
40.32
< 0.1%

bruising
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
1636 
True
 
741
(Missing)
7723 
ValueCountFrequency (%)
False1636
 
16.2%
True741
 
7.3%
(Missing)7723
76.5%
2021-01-24T11:51:12.171422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

chills
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.2%
Missing9490
Missing (%)94.0%
Memory size79.0 KiB
True
 
610
(Missing)
9490 
ValueCountFrequency (%)
True610
 
6.0%
(Missing)9490
94.0%
2021-01-24T11:51:12.209481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

clinical_shock
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2365 
True
 
10
(Missing)
7725 
ValueCountFrequency (%)
False2365
 
23.4%
True10
 
0.1%
(Missing)7725
76.5%
2021-01-24T11:51:12.240505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

conjunctival
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2045 
True
 
330
(Missing)
7725 
ValueCountFrequency (%)
False2045
 
20.2%
True330
 
3.3%
(Missing)7725
76.5%
2021-01-24T11:51:12.275379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

convulsions
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2373 
True
 
2
(Missing)
7725 
ValueCountFrequency (%)
False2373
 
23.5%
True2
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:12.311676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

cough
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.3%
Missing9738
Missing (%)96.4%
Memory size79.0 KiB
True
 
362
(Missing)
9738 
ValueCountFrequency (%)
True362
 
3.6%
(Missing)9738
96.4%
2021-01-24T11:51:12.345963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatine_kinase
Real number (ℝ≥0)

MISSING

Distinct273
Distinct (%)12.1%
Missing7842
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean74.84588131
Minimum8
Maximum1404
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:12.417332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile21
Q134
median49
Q375.75
95-th percentile204.15
Maximum1404
Range1396
Interquartile range (IQR)41.75

Descriptive statistics

Standard deviation99.67445541
Coefficient of variation (CV)1.33172933
Kurtosis56.07149742
Mean74.84588131
Median Absolute Deviation (MAD)19
Skewness6.311617762
Sum169002
Variance9934.997061
MonotocityNot monotonic
2021-01-24T11:51:12.549409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3748
 
0.5%
3548
 
0.5%
4048
 
0.5%
3145
 
0.4%
4244
 
0.4%
3443
 
0.4%
4641
 
0.4%
2740
 
0.4%
2838
 
0.4%
4538
 
0.4%
Other values (263)1825
 
18.1%
(Missing)7842
77.6%
ValueCountFrequency (%)
81
 
< 0.1%
91
 
< 0.1%
114
< 0.1%
121
 
< 0.1%
132
< 0.1%
ValueCountFrequency (%)
14041
< 0.1%
13471
< 0.1%
12641
< 0.1%
11361
< 0.1%
9671
< 0.1%

dbp
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
nan
8533 
60.0
1563 
55.0
 
2
50.0
 
2

Length

Max length4
Median length3
Mean length3.155148515
Min length3

Characters and Unicode

Total characters31867
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan8533
84.5%
60.01563
 
15.5%
55.02
 
< 0.1%
50.02
 
< 0.1%
2021-01-24T11:51:12.760632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:51:12.827546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan8533
84.5%
60.01563
 
15.5%
55.02
 
< 0.1%
50.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n17066
53.6%
a8533
26.8%
03132
 
9.8%
.1567
 
4.9%
61563
 
4.9%
56
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25599
80.3%
Decimal Number4701
 
14.8%
Other Punctuation1567
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
03132
66.6%
61563
33.2%
56
 
0.1%
ValueCountFrequency (%)
n17066
66.7%
a8533
33.3%
ValueCountFrequency (%)
.1567
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25599
80.3%
Common6268
 
19.7%

Most frequent character per script

ValueCountFrequency (%)
03132
50.0%
.1567
25.0%
61563
24.9%
56
 
0.1%
ValueCountFrequency (%)
n17066
66.7%
a8533
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII31867
100.0%

Most frequent character per block

ValueCountFrequency (%)
n17066
53.6%
a8533
26.8%
03132
 
9.8%
.1567
 
4.9%
61563
 
4.9%
56
 
< 0.1%

diarrhoea
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.4%
Missing9818
Missing (%)97.2%
Memory size79.0 KiB
True
 
282
(Missing)
9818 
ValueCountFrequency (%)
True282
 
2.8%
(Missing)9818
97.2%
2021-01-24T11:51:12.871449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_admission
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.2%
Missing9436
Missing (%)93.4%
Memory size79.0 KiB
True
 
664
(Missing)
9436 
ValueCountFrequency (%)
True664
 
6.6%
(Missing)9436
93.4%
2021-01-24T11:51:12.903541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_enrolment
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.2%
Missing9436
Missing (%)93.4%
Memory size79.0 KiB
True
 
664
(Missing)
9436 
ValueCountFrequency (%)
True664
 
6.6%
(Missing)9436
93.4%
2021-01-24T11:51:12.933858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

feeling_faint
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.2%
Missing9636
Missing (%)95.4%
Memory size79.0 KiB
True
 
464
(Missing)
9636 
ValueCountFrequency (%)
True464
 
4.6%
(Missing)9636
95.4%
2021-01-24T11:51:12.962716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fetal
Boolean

MISSING

Distinct2
Distinct (%)0.4%
Missing9536
Missing (%)94.4%
Memory size79.0 KiB
True
 
560
False
 
4
(Missing)
9536 
ValueCountFrequency (%)
True560
 
5.5%
False4
 
< 0.1%
(Missing)9536
94.4%
2021-01-24T11:51:12.994717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fibrinogen
Real number (ℝ≥0)

MISSING

Distinct352
Distinct (%)15.8%
Missing7873
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean2.893385721
Minimum0.46
Maximum7.64
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:13.072060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile1.82
Q12.34
median2.77
Q33.285
95-th percentile4.43
Maximum7.64
Range7.18
Interquartile range (IQR)0.945

Descriptive statistics

Standard deviation0.8309164426
Coefficient of variation (CV)0.2871779026
Kurtosis2.767363604
Mean2.893385721
Median Absolute Deviation (MAD)0.46
Skewness1.213297924
Sum6443.57
Variance0.6904221346
MonotocityNot monotonic
2021-01-24T11:51:13.213791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7627
 
0.3%
2.8926
 
0.3%
2.8525
 
0.2%
2.6725
 
0.2%
2.5724
 
0.2%
2.4721
 
0.2%
2.4621
 
0.2%
3.2921
 
0.2%
2.5421
 
0.2%
2.8321
 
0.2%
Other values (342)1995
 
19.8%
(Missing)7873
78.0%
ValueCountFrequency (%)
0.461
< 0.1%
0.891
< 0.1%
1.031
< 0.1%
1.061
< 0.1%
1.121
< 0.1%
ValueCountFrequency (%)
7.641
< 0.1%
6.971
< 0.1%
6.852
< 0.1%
6.591
< 0.1%
6.421
< 0.1%

haematocrit
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct19
Distinct (%)44.2%
Missing10057
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean43.58139535
Minimum30
Maximum55
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:13.315824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35
Q140
median43
Q348.5
95-th percentile52
Maximum55
Range25
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.807353951
Coefficient of variation (CV)0.1332530522
Kurtosis-0.3344634513
Mean43.58139535
Median Absolute Deviation (MAD)5
Skewness-0.1323457476
Sum1874
Variance33.72535991
MonotocityNot monotonic
2021-01-24T11:51:13.926147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
424
 
< 0.1%
454
 
< 0.1%
494
 
< 0.1%
414
 
< 0.1%
383
 
< 0.1%
403
 
< 0.1%
482
 
< 0.1%
372
 
< 0.1%
502
 
< 0.1%
442
 
< 0.1%
Other values (9)13
 
0.1%
(Missing)10057
99.6%
ValueCountFrequency (%)
301
 
< 0.1%
321
 
< 0.1%
352
< 0.1%
372
< 0.1%
383
< 0.1%
ValueCountFrequency (%)
551
< 0.1%
541
< 0.1%
522
< 0.1%
511
< 0.1%
502
< 0.1%

haematocrit_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct240
Distinct (%)5.5%
Missing5743
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean37.33218958
Minimum22.8
Maximum54.15
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:14.034037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum22.8
5-th percentile30.3
Q134.6
median37.5
Q340
95-th percentile43.9
Maximum54.15
Range31.35
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation4.14872744
Coefficient of variation (CV)0.1111300325
Kurtosis0.1905244926
Mean37.33218958
Median Absolute Deviation (MAD)2.7
Skewness-0.06158883624
Sum162656.35
Variance17.21193937
MonotocityNot monotonic
2021-01-24T11:51:14.153275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.457
 
0.6%
36.153
 
0.5%
37.652
 
0.5%
3952
 
0.5%
37.252
 
0.5%
39.551
 
0.5%
37.150
 
0.5%
38.750
 
0.5%
36.749
 
0.5%
39.349
 
0.5%
Other values (230)3842
38.0%
(Missing)5743
56.9%
ValueCountFrequency (%)
22.81
< 0.1%
23.21
< 0.1%
23.61
< 0.1%
24.82
< 0.1%
25.42
< 0.1%
ValueCountFrequency (%)
54.151
 
< 0.1%
52.53
< 0.1%
52.41
 
< 0.1%
52.21
 
< 0.1%
521
 
< 0.1%

haemoglobin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct105
Distinct (%)2.4%
Missing5743
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean12.33187055
Minimum6.8
Maximum18.5
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:14.266666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile9.98
Q111.4
median12.4
Q313.2
95-th percentile14.5
Maximum18.5
Range11.7
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.392986883
Coefficient of variation (CV)0.1129582797
Kurtosis0.3528813618
Mean12.33187055
Median Absolute Deviation (MAD)0.9
Skewness-0.064014948
Sum53729.96
Variance1.940412455
MonotocityNot monotonic
2021-01-24T11:51:14.397699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5157
 
1.6%
12.7144
 
1.4%
12.3140
 
1.4%
12.9135
 
1.3%
13132
 
1.3%
12125
 
1.2%
12.2123
 
1.2%
12.4122
 
1.2%
12.8119
 
1.2%
12.6118
 
1.2%
Other values (95)3042
30.1%
(Missing)5743
56.9%
ValueCountFrequency (%)
6.81
< 0.1%
7.11
< 0.1%
7.41
< 0.1%
7.441
< 0.1%
7.51
< 0.1%
ValueCountFrequency (%)
18.51
< 0.1%
181
< 0.1%
17.61
< 0.1%
17.41
< 0.1%
17.12
< 0.1%

headache_level
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
nan
9460 
2.0
 
393
1.0
 
150
3.0
 
97

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30300
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan9460
93.7%
2.0393
 
3.9%
1.0150
 
1.5%
3.097
 
1.0%
2021-01-24T11:51:14.590507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:51:14.647404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan9460
93.7%
2.0393
 
3.9%
1.0150
 
1.5%
3.097
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n18920
62.4%
a9460
31.2%
.640
 
2.1%
0640
 
2.1%
2393
 
1.3%
1150
 
0.5%
397
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28380
93.7%
Decimal Number1280
 
4.2%
Other Punctuation640
 
2.1%

Most frequent character per category

ValueCountFrequency (%)
0640
50.0%
2393
30.7%
1150
 
11.7%
397
 
7.6%
ValueCountFrequency (%)
n18920
66.7%
a9460
33.3%
ValueCountFrequency (%)
.640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28380
93.7%
Common1920
 
6.3%

Most frequent character per script

ValueCountFrequency (%)
.640
33.3%
0640
33.3%
2393
20.5%
1150
 
7.8%
397
 
5.1%
ValueCountFrequency (%)
n18920
66.7%
a9460
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30300
100.0%

Most frequent character per block

ValueCountFrequency (%)
n18920
62.4%
a9460
31.2%
.640
 
2.1%
0640
 
2.1%
2393
 
1.3%
1150
 
0.5%
397
 
0.3%

hematemesis
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2372 
True
 
5
(Missing)
7723 
ValueCountFrequency (%)
False2372
 
23.5%
True5
 
< 0.1%
(Missing)7723
76.5%
2021-01-24T11:51:14.692738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hematuria
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2370 
True
 
7
(Missing)
7723 
ValueCountFrequency (%)
False2370
 
23.5%
True7
 
0.1%
(Missing)7723
76.5%
2021-01-24T11:51:14.728090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

inr
Real number (ℝ≥0)

MISSING

Distinct44
Distinct (%)1.9%
Missing7835
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean1.024379691
Minimum1
Maximum4.04
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:14.802601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.01
95-th percentile1.14
Maximum4.04
Range3.04
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.08710537835
Coefficient of variation (CV)0.08503231675
Kurtosis640.767858
Mean1.024379691
Median Absolute Deviation (MAD)0
Skewness19.77231261
Sum2320.22
Variance0.007587346937
MonotocityNot monotonic
2021-01-24T11:51:14.918285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11663
 
16.5%
1.0282
 
0.8%
1.0170
 
0.7%
1.0448
 
0.5%
1.0343
 
0.4%
1.0539
 
0.4%
1.0937
 
0.4%
1.0634
 
0.3%
1.0730
 
0.3%
1.129
 
0.3%
Other values (34)190
 
1.9%
(Missing)7835
77.6%
ValueCountFrequency (%)
11663
16.5%
1.0170
 
0.7%
1.0282
 
0.8%
1.0343
 
0.4%
1.0448
 
0.5%
ValueCountFrequency (%)
4.041
< 0.1%
1.871
< 0.1%
1.521
< 0.1%
1.481
< 0.1%
1.441
< 0.1%

jaundice
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2372 
True
 
3
(Missing)
7725 
ValueCountFrequency (%)
False2372
 
23.5%
True3
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:14.988535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

joint_pain_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
nan
9544 
2.0
 
356
1.0
 
150
3.0
 
50

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30300
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan9544
94.5%
2.0356
 
3.5%
1.0150
 
1.5%
3.050
 
0.5%
2021-01-24T11:51:15.145016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:51:15.203005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan9544
94.5%
2.0356
 
3.5%
1.0150
 
1.5%
3.050
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n19088
63.0%
a9544
31.5%
.556
 
1.8%
0556
 
1.8%
2356
 
1.2%
1150
 
0.5%
350
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28632
94.5%
Decimal Number1112
 
3.7%
Other Punctuation556
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
0556
50.0%
2356
32.0%
1150
 
13.5%
350
 
4.5%
ValueCountFrequency (%)
n19088
66.7%
a9544
33.3%
ValueCountFrequency (%)
.556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28632
94.5%
Common1668
 
5.5%

Most frequent character per script

ValueCountFrequency (%)
.556
33.3%
0556
33.3%
2356
21.3%
1150
 
9.0%
350
 
3.0%
ValueCountFrequency (%)
n19088
66.7%
a9544
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30300
100.0%

Most frequent character per block

ValueCountFrequency (%)
n19088
63.0%
a9544
31.5%
.556
 
1.8%
0556
 
1.8%
2356
 
1.2%
1150
 
0.5%
350
 
0.2%

lethargy
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2369 
True
 
6
(Missing)
7725 
ValueCountFrequency (%)
False2369
 
23.5%
True6
 
0.1%
(Missing)7725
76.5%
2021-01-24T11:51:15.249137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_palpation
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2325 
True
 
50
(Missing)
7725 
ValueCountFrequency (%)
False2325
 
23.0%
True50
 
0.5%
(Missing)7725
76.5%
2021-01-24T11:51:15.284413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_size
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
nan
10050 
2.0
 
28
1.0
 
19
18.0
 
2
14.0
 
1

Length

Max length4
Median length3
Mean length3.00029703
Min length3

Characters and Unicode

Total characters30303
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan10050
99.5%
2.028
 
0.3%
1.019
 
0.2%
18.02
 
< 0.1%
14.01
 
< 0.1%
2021-01-24T11:51:15.453272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:51:15.512130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan10050
99.5%
2.028
 
0.3%
1.019
 
0.2%
18.02
 
< 0.1%
14.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n20100
66.3%
a10050
33.2%
.50
 
0.2%
050
 
0.2%
228
 
0.1%
122
 
0.1%
82
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter30150
99.5%
Decimal Number103
 
0.3%
Other Punctuation50
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
050
48.5%
228
27.2%
122
21.4%
82
 
1.9%
41
 
1.0%
ValueCountFrequency (%)
n20100
66.7%
a10050
33.3%
ValueCountFrequency (%)
.50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30150
99.5%
Common153
 
0.5%

Most frequent character per script

ValueCountFrequency (%)
.50
32.7%
050
32.7%
228
18.3%
122
14.4%
82
 
1.3%
41
 
0.7%
ValueCountFrequency (%)
n20100
66.7%
a10050
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30303
100.0%

Most frequent character per block

ValueCountFrequency (%)
n20100
66.3%
a10050
33.2%
.50
 
0.2%
050
 
0.2%
228
 
0.1%
122
 
0.1%
82
 
< 0.1%
41
 
< 0.1%

lymphadenopathy
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2357 
True
 
18
(Missing)
7725 
ValueCountFrequency (%)
False2357
 
23.3%
True18
 
0.2%
(Missing)7725
76.5%
2021-01-24T11:51:15.571826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

lymphocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct667
Distinct (%)15.3%
Missing5744
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean33.00003214
Minimum1.4
Maximum77.9
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:15.656600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile10.9
Q121.6
median32.05
Q343
95-th percentile59.1
Maximum77.9
Range76.5
Interquartile range (IQR)21.4

Descriptive statistics

Standard deviation14.63303001
Coefficient of variation (CV)0.44342472
Kurtosis-0.4589838512
Mean33.00003214
Median Absolute Deviation (MAD)10.65
Skewness0.3311404581
Sum143748.14
Variance214.1255673
MonotocityNot monotonic
2021-01-24T11:51:15.783923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.523
 
0.2%
21.619
 
0.2%
42.919
 
0.2%
28.818
 
0.2%
32.417
 
0.2%
36.417
 
0.2%
21.516
 
0.2%
33.916
 
0.2%
33.416
 
0.2%
23.616
 
0.2%
Other values (657)4179
41.4%
(Missing)5744
56.9%
ValueCountFrequency (%)
1.41
< 0.1%
1.51
< 0.1%
2.11
< 0.1%
3.21
< 0.1%
3.91
< 0.1%
ValueCountFrequency (%)
77.91
< 0.1%
771
< 0.1%
76.41
< 0.1%
75.31
< 0.1%
74.71
< 0.1%

melaena
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2374 
True
 
3
(Missing)
7723 
ValueCountFrequency (%)
False2374
 
23.5%
True3
 
< 0.1%
(Missing)7723
76.5%
2021-01-24T11:51:15.856582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

meningism
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2373 
True
 
2
(Missing)
7725 
ValueCountFrequency (%)
False2373
 
23.5%
True2
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:15.896472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

monocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct215
Distinct (%)4.9%
Missing5744
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean7.396997245
Minimum0.3
Maximum42.8
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:15.978131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile3.4
Q15.3
median6.8
Q38.9
95-th percentile13.1
Maximum42.8
Range42.5
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation3.236674433
Coefficient of variation (CV)0.4375659914
Kurtosis10.68496379
Mean7.396997245
Median Absolute Deviation (MAD)1.7
Skewness2.005638965
Sum32221.32
Variance10.47606138
MonotocityNot monotonic
2021-01-24T11:51:16.095508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.484
 
0.8%
6.678
 
0.8%
5.578
 
0.8%
5.977
 
0.8%
5.876
 
0.8%
5.176
 
0.8%
5.475
 
0.7%
6.974
 
0.7%
6.374
 
0.7%
6.272
 
0.7%
Other values (205)3592
35.6%
(Missing)5744
56.9%
ValueCountFrequency (%)
0.31
< 0.1%
0.81
< 0.1%
11
< 0.1%
1.31
< 0.1%
1.52
< 0.1%
ValueCountFrequency (%)
42.81
< 0.1%
371
< 0.1%
35.61
< 0.1%
35.31
< 0.1%
30.31
< 0.1%

muscle_pain_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
nan
9497 
2.0
 
381
1.0
 
166
3.0
 
56

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30300
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan9497
94.0%
2.0381
 
3.8%
1.0166
 
1.6%
3.056
 
0.6%
2021-01-24T11:51:16.284166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:51:16.337341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan9497
94.0%
2.0381
 
3.8%
1.0166
 
1.6%
3.056
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n18994
62.7%
a9497
31.3%
.603
 
2.0%
0603
 
2.0%
2381
 
1.3%
1166
 
0.5%
356
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28491
94.0%
Decimal Number1206
 
4.0%
Other Punctuation603
 
2.0%

Most frequent character per category

ValueCountFrequency (%)
0603
50.0%
2381
31.6%
1166
 
13.8%
356
 
4.6%
ValueCountFrequency (%)
n18994
66.7%
a9497
33.3%
ValueCountFrequency (%)
.603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28491
94.0%
Common1809
 
6.0%

Most frequent character per script

ValueCountFrequency (%)
.603
33.3%
0603
33.3%
2381
21.1%
1166
 
9.2%
356
 
3.1%
ValueCountFrequency (%)
n18994
66.7%
a9497
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30300
100.0%

Most frequent character per block

ValueCountFrequency (%)
n18994
62.7%
a9497
31.3%
.603
 
2.0%
0603
 
2.0%
2381
 
1.3%
1166
 
0.5%
356
 
0.2%

nausea
Boolean

MISSING

Distinct2
Distinct (%)0.4%
Missing9603
Missing (%)95.1%
Memory size79.0 KiB
True
 
496
False
 
1
(Missing)
9603 
ValueCountFrequency (%)
True496
 
4.9%
False1
 
< 0.1%
(Missing)9603
95.1%
2021-01-24T11:51:16.381383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neurology
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2370 
True
 
5
(Missing)
7725 
ValueCountFrequency (%)
False2370
 
23.5%
True5
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:16.415260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neutrophils_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct746
Distinct (%)17.1%
Missing5743
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean52.80841175
Minimum6.1
Maximum96.8
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:16.493448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile24.1
Q138.6
median53.1
Q367.2
95-th percentile80.9
Maximum96.8
Range90.7
Interquartile range (IQR)28.6

Descriptive statistics

Standard deviation17.73432742
Coefficient of variation (CV)0.3358239121
Kurtosis-0.8557399342
Mean52.80841175
Median Absolute Deviation (MAD)14.3
Skewness-0.05554027401
Sum230086.25
Variance314.5063692
MonotocityNot monotonic
2021-01-24T11:51:16.610948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.315
 
0.1%
52.914
 
0.1%
45.614
 
0.1%
47.814
 
0.1%
50.714
 
0.1%
53.314
 
0.1%
66.114
 
0.1%
57.114
 
0.1%
57.514
 
0.1%
64.913
 
0.1%
Other values (736)4217
41.8%
(Missing)5743
56.9%
ValueCountFrequency (%)
6.11
< 0.1%
6.31
< 0.1%
7.91
< 0.1%
11.11
< 0.1%
11.31
< 0.1%
ValueCountFrequency (%)
96.81
< 0.1%
94.91
< 0.1%
93.41
< 0.1%
921
< 0.1%
91.92
< 0.1%

oedema
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7370
Missing (%)73.0%
Memory size79.0 KiB
False
2621 
True
 
109
(Missing)
7370 
ValueCountFrequency (%)
False2621
 
26.0%
True109
 
1.1%
(Missing)7370
73.0%
2021-01-24T11:51:16.679909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

oedema_face
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2372 
True
 
5
(Missing)
7723 
ValueCountFrequency (%)
False2372
 
23.5%
True5
 
< 0.1%
(Missing)7723
76.5%
2021-01-24T11:51:16.718319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

oedema_feet
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2371 
True
 
6
(Missing)
7723 
ValueCountFrequency (%)
False2371
 
23.5%
True6
 
0.1%
(Missing)7723
76.5%
2021-01-24T11:51:16.753122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

oedema_hands
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
2375 
True
 
2
(Missing)
7723 
ValueCountFrequency (%)
False2375
 
23.5%
True2
 
< 0.1%
(Missing)7723
76.5%
2021-01-24T11:51:16.786718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

petechiae
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7723
Missing (%)76.5%
Memory size79.0 KiB
False
1413 
True
964 
(Missing)
7723 
ValueCountFrequency (%)
False1413
 
14.0%
True964
 
9.5%
(Missing)7723
76.5%
2021-01-24T11:51:16.820726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pharyngeal
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2167 
True
 
208
(Missing)
7725 
ValueCountFrequency (%)
False2167
 
21.5%
True208
 
2.1%
(Missing)7725
76.5%
2021-01-24T11:51:16.854624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pleural_effusion
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2372 
True
 
3
(Missing)
7725 
ValueCountFrequency (%)
False2372
 
23.5%
True3
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:16.886967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

plt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct486
Distinct (%)11.2%
Missing5744
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean126.0206382
Minimum3
Maximum619
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:16.962026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile21
Q159
median101
Q3158
95-th percentile346
Maximum619
Range616
Interquartile range (IQR)99

Descriptive statistics

Standard deviation97.83918779
Coefficient of variation (CV)0.776374324
Kurtosis2.681058686
Mean126.0206382
Median Absolute Deviation (MAD)48
Skewness1.585932398
Sum548945.9
Variance9572.506667
MonotocityNot monotonic
2021-01-24T11:51:17.075315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9636
 
0.4%
7035
 
0.3%
8734
 
0.3%
2733
 
0.3%
8333
 
0.3%
6933
 
0.3%
11532
 
0.3%
9431
 
0.3%
3131
 
0.3%
8131
 
0.3%
Other values (476)4027
39.9%
(Missing)5744
56.9%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
4.81
 
< 0.1%
51
 
< 0.1%
65
< 0.1%
ValueCountFrequency (%)
6191
< 0.1%
5931
< 0.1%
5911
< 0.1%
5891
< 0.1%
5721
< 0.1%

pulse
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)1.7%
Missing7453
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean90.19569324
Minimum66
Maximum128
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:17.190192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile80
Q184
median88
Q394
95-th percentile106
Maximum128
Range62
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.296349471
Coefficient of variation (CV)0.0919816587
Kurtosis1.478700907
Mean90.19569324
Median Absolute Deviation (MAD)4
Skewness0.8691030529
Sum238748
Variance68.82941455
MonotocityNot monotonic
2021-01-24T11:51:17.315660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
88348
 
3.4%
86336
 
3.3%
90324
 
3.2%
84277
 
2.7%
100200
 
2.0%
92193
 
1.9%
80154
 
1.5%
96142
 
1.4%
94134
 
1.3%
8299
 
1.0%
Other values (35)440
 
4.4%
(Missing)7453
73.8%
ValueCountFrequency (%)
661
 
< 0.1%
682
 
< 0.1%
708
0.1%
7210
0.1%
7413
0.1%
ValueCountFrequency (%)
1281
 
< 0.1%
1263
 
< 0.1%
1242
 
< 0.1%
1226
0.1%
1208
0.1%

rales_crackles
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2371 
True
 
4
(Missing)
7725 
ValueCountFrequency (%)
False2371
 
23.5%
True4
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:17.388270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_distress
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2373 
True
 
2
(Missing)
7725 
ValueCountFrequency (%)
False2373
 
23.5%
True2
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:17.428720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_rate
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)0.5%
Missing7453
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean20.51681148
Minimum15
Maximum28
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:17.486697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20
Q120
median20
Q320
95-th percentile24
Maximum28
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.205147985
Coefficient of variation (CV)0.05873953592
Kurtosis6.537512413
Mean20.51681148
Median Absolute Deviation (MAD)0
Skewness2.178163277
Sum54308
Variance1.452381666
MonotocityNot monotonic
2021-01-24T11:51:17.588319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
202082
 
20.6%
22395
 
3.9%
24127
 
1.3%
1813
 
0.1%
268
 
0.1%
287
 
0.1%
175
 
< 0.1%
234
 
< 0.1%
192
 
< 0.1%
151
 
< 0.1%
Other values (3)3
 
< 0.1%
(Missing)7453
73.8%
ValueCountFrequency (%)
151
 
< 0.1%
161
 
< 0.1%
175
 
< 0.1%
1813
0.1%
192
 
< 0.1%
ValueCountFrequency (%)
287
 
0.1%
268
 
0.1%
251
 
< 0.1%
24127
1.3%
234
 
< 0.1%

restlessness
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2374 
True
 
1
(Missing)
7725 
ValueCountFrequency (%)
False2374
 
23.5%
True1
 
< 0.1%
(Missing)7725
76.5%
2021-01-24T11:51:17.648978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

rhinitis
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2339 
True
 
36
(Missing)
7725 
ValueCountFrequency (%)
False2339
 
23.2%
True36
 
0.4%
(Missing)7725
76.5%
2021-01-24T11:51:17.686543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sbp
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)0.5%
Missing7453
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean101.7476388
Minimum85
Maximum140
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:17.742686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile90
Q1100
median100
Q3110
95-th percentile120
Maximum140
Range55
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.443297662
Coefficient of variation (CV)0.08298273806
Kurtosis0.3391123559
Mean101.7476388
Median Absolute Deviation (MAD)10
Skewness0.5211090295
Sum269326
Variance71.28927541
MonotocityNot monotonic
2021-01-24T11:51:17.821612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1001260
 
12.5%
110674
 
6.7%
90547
 
5.4%
120140
 
1.4%
13014
 
0.1%
953
 
< 0.1%
1403
 
< 0.1%
1052
 
< 0.1%
1151
 
< 0.1%
961
 
< 0.1%
Other values (2)2
 
< 0.1%
(Missing)7453
73.8%
ValueCountFrequency (%)
851
 
< 0.1%
90547
5.4%
953
 
< 0.1%
961
 
< 0.1%
1001260
12.5%
ValueCountFrequency (%)
1403
 
< 0.1%
13014
 
0.1%
1251
 
< 0.1%
120140
1.4%
1151
 
< 0.1%

skin_flush
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2158 
True
 
217
(Missing)
7725 
ValueCountFrequency (%)
False2158
 
21.4%
True217
 
2.1%
(Missing)7725
76.5%
2021-01-24T11:51:17.878574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_rash
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
1857 
True
 
518
(Missing)
7725 
ValueCountFrequency (%)
False1857
 
18.4%
True518
 
5.1%
(Missing)7725
76.5%
2021-01-24T11:51:17.915866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sore_throat
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.5%
Missing9912
Missing (%)98.1%
Memory size79.0 KiB
True
 
188
(Missing)
9912 
ValueCountFrequency (%)
True188
 
1.9%
(Missing)9912
98.1%
2021-01-24T11:51:17.948780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

spleen_palpation
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing7725
Missing (%)76.5%
Memory size79.0 KiB
False
2375 
(Missing)
7725 
ValueCountFrequency (%)
False2375
 
23.5%
(Missing)7725
76.5%
2021-01-24T11:51:17.977748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

tck
Real number (ℝ≥0)

MISSING

Distinct306
Distinct (%)13.7%
Missing7874
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean38.59604672
Minimum21.3
Maximum88.1
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:18.054063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum21.3
5-th percentile29.6
Q133.8
median37.8
Q342.2
95-th percentile49.9
Maximum88.1
Range66.8
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation6.823288779
Coefficient of variation (CV)0.1767872453
Kurtosis4.512273081
Mean38.59604672
Median Absolute Deviation (MAD)4.2
Skewness1.328285292
Sum85914.8
Variance46.55726976
MonotocityNot monotonic
2021-01-24T11:51:18.179758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.424
 
0.2%
32.521
 
0.2%
38.721
 
0.2%
36.920
 
0.2%
34.120
 
0.2%
33.919
 
0.2%
33.619
 
0.2%
38.818
 
0.2%
35.818
 
0.2%
38.117
 
0.2%
Other values (296)2029
 
20.1%
(Missing)7874
78.0%
ValueCountFrequency (%)
21.31
< 0.1%
231
< 0.1%
23.41
< 0.1%
23.72
< 0.1%
24.61
< 0.1%
ValueCountFrequency (%)
88.11
< 0.1%
781
< 0.1%
77.71
< 0.1%
76.21
< 0.1%
75.61
< 0.1%

tq
Real number (ℝ≥0)

MISSING

Distinct68
Distinct (%)3.0%
Missing7870
Missing (%)77.9%
Infinite0
Infinite (%)0.0%
Mean12.56179372
Minimum10.3
Maximum40.3
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:18.302157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile11.3
Q111.9
median12.4
Q313
95-th percentile14.355
Maximum40.3
Range30
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.139830063
Coefficient of variation (CV)0.09073784273
Kurtosis157.7731461
Mean12.56179372
Median Absolute Deviation (MAD)0.6
Skewness7.212666
Sum28012.8
Variance1.299212573
MonotocityNot monotonic
2021-01-24T11:51:18.420912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.2120
 
1.2%
12.1115
 
1.1%
12.5114
 
1.1%
12.3113
 
1.1%
12.4110
 
1.1%
11.9108
 
1.1%
12104
 
1.0%
12.6101
 
1.0%
11.894
 
0.9%
11.684
 
0.8%
Other values (58)1167
 
11.6%
(Missing)7870
77.9%
ValueCountFrequency (%)
10.31
 
< 0.1%
10.41
 
< 0.1%
10.61
 
< 0.1%
10.74
 
< 0.1%
10.810
0.1%
ValueCountFrequency (%)
40.31
< 0.1%
18.21
< 0.1%
17.81
< 0.1%
17.31
< 0.1%
17.21
< 0.1%

uterus_tender
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing9173
Missing (%)90.8%
Memory size79.0 KiB
False
921 
True
 
6
(Missing)
9173 
ValueCountFrequency (%)
False921
 
9.1%
True6
 
0.1%
(Missing)9173
90.8%
2021-01-24T11:51:18.488736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vagina_loss
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing9168
Missing (%)90.8%
Memory size79.0 KiB
False
 
902
True
 
30
(Missing)
9168 
ValueCountFrequency (%)
False902
 
8.9%
True30
 
0.3%
(Missing)9168
90.8%
2021-01-24T11:51:18.524540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting_level
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
nan
9718 
1.0
 
274
2.0
 
100
3.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30300
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan9718
96.2%
1.0274
 
2.7%
2.0100
 
1.0%
3.08
 
0.1%
2021-01-24T11:51:18.678581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:51:18.733649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan9718
96.2%
1.0274
 
2.7%
2.0100
 
1.0%
3.08
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n19436
64.1%
a9718
32.1%
.382
 
1.3%
0382
 
1.3%
1274
 
0.9%
2100
 
0.3%
38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29154
96.2%
Decimal Number764
 
2.5%
Other Punctuation382
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
0382
50.0%
1274
35.9%
2100
 
13.1%
38
 
1.0%
ValueCountFrequency (%)
n19436
66.7%
a9718
33.3%
ValueCountFrequency (%)
.382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29154
96.2%
Common1146
 
3.8%

Most frequent character per script

ValueCountFrequency (%)
.382
33.3%
0382
33.3%
1274
23.9%
2100
 
8.7%
38
 
0.7%
ValueCountFrequency (%)
n19436
66.7%
a9718
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30300
100.0%

Most frequent character per block

ValueCountFrequency (%)
n19436
64.1%
a9718
32.1%
.382
 
1.3%
0382
 
1.3%
1274
 
0.9%
2100
 
0.3%
38
 
< 0.1%

wbc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct998
Distinct (%)22.9%
Missing5743
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean5.140460179
Minimum0.72
Maximum24.25
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:18.820539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.72
5-th percentile1.8
Q13.2
median4.8
Q36.6
95-th percentile9.9
Maximum24.25
Range23.53
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.551745248
Coefficient of variation (CV)0.4964040493
Kurtosis1.903078458
Mean5.140460179
Median Absolute Deviation (MAD)1.7
Skewness1.007880156
Sum22396.985
Variance6.511403811
MonotocityNot monotonic
2021-01-24T11:51:18.946998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.830
 
0.3%
3.228
 
0.3%
328
 
0.3%
3.328
 
0.3%
2.426
 
0.3%
3.426
 
0.3%
5.326
 
0.3%
3.926
 
0.3%
3.626
 
0.3%
5.425
 
0.2%
Other values (988)4088
40.5%
(Missing)5743
56.9%
ValueCountFrequency (%)
0.721
< 0.1%
0.731
< 0.1%
0.751
< 0.1%
0.771
< 0.1%
0.82
< 0.1%
ValueCountFrequency (%)
24.251
< 0.1%
21.341
< 0.1%
18.41
< 0.1%
17.71
< 0.1%
16.921
< 0.1%

weight
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct56
Distinct (%)1.6%
Missing6669
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean59.78796269
Minimum39
Maximum93
Zeros0
Zeros (%)0.0%
Memory size79.0 KiB
2021-01-24T11:51:19.069274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile45.5
Q155
median60
Q365
95-th percentile74
Maximum93
Range54
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.558127313
Coefficient of variation (CV)0.1431413102
Kurtosis0.9608959065
Mean59.78796269
Median Absolute Deviation (MAD)5
Skewness0.322841734
Sum205132.5
Variance73.24154311
MonotocityNot monotonic
2021-01-24T11:51:19.197609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60320
 
3.2%
62224
 
2.2%
57214
 
2.1%
53158
 
1.6%
63151
 
1.5%
68143
 
1.4%
55141
 
1.4%
65140
 
1.4%
51123
 
1.2%
50121
 
1.2%
Other values (46)1696
 
16.8%
(Missing)6669
66.0%
ValueCountFrequency (%)
3918
 
0.2%
4017
 
0.2%
4262
0.6%
4335
0.3%
4421
 
0.2%
ValueCountFrequency (%)
9310
0.1%
9016
0.2%
871
 
< 0.1%
8516
0.2%
831
 
< 0.1%

Interactions

2021-01-24T11:50:24.294644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.389554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.475083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.559979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.636375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.720487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.807391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.880096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:24.965443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:25.050716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:25.134304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:25.314115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:25.406364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:25.489272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:25.571882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:25.659334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-24T11:50:58.258349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.331495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.416739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.497992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.587523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.665254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.742014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.818369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.895056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:58.961009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.045029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.123981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.187882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.271696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.349503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.429594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.513616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.590974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.666621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.745355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.809366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.874617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:50:59.943527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.028643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.109241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.187607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.263804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.345069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.433672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.518637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.601544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.693328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.780419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.846359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:00.927581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.006828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.096299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.177949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.257933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.343388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.427145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.506782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.590286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.668728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.754694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:51:01.841136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-24T11:51:19.346879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-24T11:51:19.599193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-24T11:51:19.837486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-24T11:51:20.147694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-01-24T11:51:02.726406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-24T11:51:04.939751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-24T11:51:06.716800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-24T11:51:09.387711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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